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List of TAO Seminars (reverse chronological order) Tao
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Seminars


The page below lists the coming and past seminars, and provides a link to the presentations that you may have missed.
Alert emails are sent to the TAU team and to the announcement mailing-list tau-seminars at inria.fr, to which anyone can subscribe by clicking here .
Some of these presentations are organized with the GT Deep Net ; to subscribe to the related announcement mailing-list, click there .

You may also be interested in the Seminaires d'apprentissage et de statistique de l'universite Paris Saclay
Google Calendar Ical format Google group


(Updated!) Until the end of 2017, all seminars take place on Wednesday at 14h30 in room 2014 (building 660), unless specified otherwise. From January 2018, they will take place on Tuesday again.
The presentations are recorded and available here .


2018

January

  • January, Friday 19th, whole day (IHES): workshop stats maths/info du plateau de Saclay

2017

December

  • December, Wednesday 13th, 14:30 (room 445, building 650): Robin Girard (Mines ParisTech Sophia-Antipolis) (more information...)
  • December, first week: break (NIPS)

November

  • November, Wednesday 22th, 14:30 (room 2014): Marylou Gabrié (ENS Paris, Laboratoire de Physique Statistique): Mean-Field Framework for Unsupervised Learning with Boltzmann Machines (more information...)
  • November, Friday 17th, 11:00 (Shannon amphitheatre): [ GT DeepNet ] Levent Sagun (IPHT Saclay): Over-Parametrization in Deep Learning (more information...)
  • November, Wednesday 15th, 14:30 (room 2014): Diviyan Kalainathan & Olivier Goudet (TAU): Causal Generative Neural Networks (more information...)
  • November, Thursday 9th, 11:00 (Shannon amphitheatre): Claire Monteleoni (CNRS-LAL / George Washington University): Machine Learning Algorithms for Climate Informatics, Sustainability, and Social Good (more information...)

October

  • October, Tuesday 24th, 14:30 (Shannon amphitheatre): Benjamin Guedj (MODAL team, Inria Lille): A quasi-Bayesian perspective to NMF: theory and applications (more information...)
  • October, Wednesday 18th, 14:30 (room 2014): Théophile Sanchez (TAU): End-to-end Deep Learning Approach for Demographic History Inference (more information...)
  • October, Wednesday 11th, 14:00 (room 2014): Victor Estrade (TAU): Robust Deep Learning : A case study (more information...)
  • October, Wednesday 4th, 14:30 (room 2014): Hugo Richard (Parietal/TAU): Data based alignment of brain fmri images (more information...)

September

  • September, Tuesday 19th, 11:00 (Shannon amphitheatre): Carlo Lucibello (Politecnico di Torino): Probing the energy landscape of Artificial Neural Networks (more information...)

July

  • July, Tuesday 4th, from 11:00 to 13:00 (Shannon amphitheatre): presentation of Brice Bathellier's team + MLspike by Thomas Deneux (more information...)

June

  • June, Friday 30th, 14:30 (room 2014): internships presentation by Giancarlo Fissore: Learning dynamics of Restricted Boltzmann Machines, and by Clément Leroy: Free Energy Landscape in a Restricted Boltzmann Machine (RBM) (more information...)
  • June, Thursday 29th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Alexandre Barachant: Information Geometry: A framework for manipulation and classification of neural time series (more information...)
  • June, Tuesday 27th, 14:30 (room 2014) Réda Alami et Raphaël Féraud (Orange Labs): Memory Bandits : A bayesian Approach for the Switching Bandit Problem (more information...)
  • June, Monday 12th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Romain Couillet (Centrale-Supélec): A Random Matrix Framework for BigData Machine Learning (more information...)

May

  • May, Wednesday 24th, 16:00 (room 2014): Priyanka Mandikal (TAU): Anatomy Localization in Medical Images using Neural Networks (more information...)

April

  • April, Friday 28th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Jascha Sohl-dickstein (Google Brain): Deep Unsupervised Learning using Nonequilibrium Thermodynamics (more information...)
  • April, Tuesday 3rd: Thomas Schmitt: RecSys challenge 2017 (more information...)

March

  • March, Thursday 2nd, 14:30 (Shannon amphitheatre): Marta Soare (Aalto University): Sequential Decision Making in Linear Bandit Setting (more information...)

February

  • February 22nd, 11h: Enrico Camporeale (CWI): Machine learning for Space-Weather forecasting
  • February, Thursday 16th (Shannon amphi.), 14h30: [ GT DeepNet ] Corentin Tallec: Unbiased Online Recurrent Optimization (more information...)
  • February 14th (Shannon amphi.), 14h: [ GT DeepNet ] Victor Berger (Thales Services, ThereSIS): VAE/GAN as a generative model (more information...)

January

  • January 25th, 10h30: Romain Julliard (Muséum National d'Histoire Naturelle): 65 Millions d'Observateurs (more information...)
  • January 24th: Daniela Pamplona (Biovision team, INRIA Sophia-Antipolis / TAO): Data Based Approaches in Retinal Models and Analysis (more information...)



2016


November

  • November 30th: Martin Riedmiller (Google DeepMind). Deep Reinforcement learning for learning machines (more information...)
  • November 29th: Amaury Habrard (Universite Jean Monnet de Saint-Etienne). Domain Adaptation with Optimal Transport: Mapping Estimation and Theory (more information...)
  • November 24th: [ GT DeepNet ] Rico Sennrich (University of Edinburgh). Neural Machine Translation: Breaking the Performance Plateau (more information...)

June

  • June 28th: Lenka Zdeborova (CEA,Ipht). Solvable models of unsupervised feature learning LRI_matrix_fact.pdf

Mai

  • May 3rd: Emile Contal (ENS-Cachan). The geometry of Gaussian processes and Bayesian optimization. slides_semstat16.pdf

April

  • April 26: Marc Bellemare (Google DeepMind). Eight Years of Research with the Atari 2600 (more information...)
  • April 12: Mikael Kuusela (EPFL). Shape-constrained uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider.(more information...)

March

  • March 22nd: Matthieu Geist (Supélec Metz): Reductions from inverse reinforcement learning to supervised learning (more information...)
  • March 15: Richard Wilkinson (University of Sheffield): Using surrogate models to accelerate parameter estimation for complex simulators (more information...)
  • March 1st: Pascal Germain (Université Laval, Québec): A Representation Learning Approach for Domain Adaptation (more information...)

February


January

  • January 26th: Laurent Massoulié: Models of collective inference.(more information...).
  • January 19th: Sébastien Gadat: Regret bounds for Narendra-Shapiro bandit algorithms (more information...)..


2015

December



November


  • November 19th: Phillipe Sampaio: A derivative-free trust-funnel method for constrained nonlinear optimization (more information...).


October



  • October 20th: Jean Lafond: Low Rank Matrix Completion with Exponential Family Noise (more information...).

  • October 13th
    • Flora Jay:Inferring past and present demography from genetic data (more information...).
    • Marcus Gallagher: Engineering Features for the Analysis and Comparison Black-box Optimization Problems and Algorithms (more information...).



September


  • Sept. 28th
    • Olivier Pietquin, Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games OlivierPietquin_ICML15.pdf
    • Francois Laviolette, Domain Adaptation (slides soon)

July




June


  • June 15th: Claire Monteleoni:Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science
  • June 2nd: Robyn Francon: Reversing Operators for Semantic Backpropagation

May

  • May 18th:Andras Gyorgy:Adaptive Monte Carlo via Bandit Allocation

April


  • April 28th:Vianney Perchet:Optimal Sample Size in Multi-Phase Learning(more information...)
  • April 27th:Hédi Soula, TBA
  • April 21th: Gregory Grefenstette, INRIA Saclay: Personal semantics(more information...)
  • April 7th: Paul Honeine: Relever deux défis majeurs en apprentissage par méthodes à noyaux:problème de pré-image et apprentissage en ligne (more information...)

March

  • March 31th: Bruno Scherrer (Inria Nancy): Non-Stationary Modified Policy Iteration (more information...)
  • March 24th: Christophe Schülke(ESPCI): Community detection with modularity: a statistical physics approach (more information...)
  • March 10th: Balazs Kegl: Rapid Analytics and Model Prototyping (more information...)

February

  • February 24th: Madalina Drugan (Vrije Universiteit Brussel, Belgium): Multi-objective multi-armed bandits (more information...)
  • February 20th: Holger Hoos (University of British Columbia, Canada): séminaire MSR - see the slides
  • February 17th :Aurélien Bellet: The Frank-Wolfe Algorithm: Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization more information...
  • February 10th, Manuel Lopes 15interlearnteach.pdf

January

  • January 27th :Raphaël Baillyra: Tensor factorization for multi-relational learning ((more information...)
  • January 13th : Francesco Caltagirone: On convergence of Approximate Message Passing (talk_Caltagirone.pdf)
  • January 6th : Emilie Kaufmann: Bayesian and frequentist strategies for sequential resource allocation (Emilie_Kauffman.pdf)

Seminars 2014

November

  • November 4th :Joaquin Vanschoren:OpenML: Networked science in machine learning

October

  • Oct. 28th,
    • Antoine Bureau, "Bellmanian Bandit Network"
This paper presents a new reinforcement learning (RL) algorithm called Bellmanian Bandit Network (BBN), where action selection in each state is formalized as a multi-armed bandit problem. The first contribution lies in the definition of an exploratory reward inspired from the intrinsic motivation criterion from -1-, combined with the RL reward. The second contribution is to use a network of multi-armed bandits to achieve the convergence toward the optimal Q-value function. The BBN algorithm is comparatively validated to -1-.
References:
-1- Manuel Lopes, Tobias Lang, Marc Toussaint, and Pierre-Yves Oudeyer. Exploration in model-based reinforcement learning by empirically estimating learning progress. In Neural Information Processing System (NIPS), 2012.

    • Basile Mayeur
Abstract:
Taking inspiration from inverse reinforcement learning, the proposed Direct Value Learning for Reinforcement Learning (DIVA) approach uses light priors to gener- ate inappropriate behavior’s, and use the corresponding state sequences to directly learn a value function. When the transition model is known, this value function directly defines a (nearly) optimal controller. Otherwise, the value function is extended to the (state,action) space using off-policy learning.
The experimental validation of DIVA on the Mountain car shows the robustness of the approach comparatively to SARSA, based on the assumption that the tar- get state is known. Lighter assumptions are considered in the Bicycle problem, showing the robustness of DIVA in a model-free setting.

    • Thomas Schmitt, "Exploration / exploitation: a free energy-based criterion"
We investigate a new strategy, based on a free energy criterion to solve the exploration/exploitation dilemma. Our strategy promotes exploration using an entropy term.


September

  • Sept. 29th, Rich Caruana

Old seminars

Contributors to this page: guillaume , furtlehn , sebag , maillard@lri.fr , cecile , evomarc , BasileMayeur , ThomasS , Antoine.Bureau , hansen , kegl and lopes .
Page last modified on Monday 13 of November, 2017 16:12:53 CET by guillaume.